GECAD-Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Rua DR. Antonio Bernardino de Almeida, 431, 4200-072 Porto, Portugal.
Polytechnic of Porto, Rua DR. Antonio Bernardino de Almeida, 431, 4200-072 Porto, Portugal.
Sensors (Basel). 2020 Jan 21;20(3):593. doi: 10.3390/s20030593.
This paper presents a multiperiod optimization algorithm that is implemented in a Supervisory Control and Data Acquisition system. The algorithm controls lights and air conditioners as flexible loads to reduce the consumption and controls a dishwasher as a deferrable load to implement the load shifting. Several parameters are considered to implement the algorithm for several successive periods in a real building operation. In the proposed methodology, optimization is done regarding user comfort, which is modeled in the objective function related to the indoor temperature in each room, and in the constraints in order to prevent excessive power reduction, according to users' preferences. Additionally, the operation cycle of a dishwasher is included, and the algorithm selects the best starting point based on the appliance weights and power availability in each period. With the proposed methodology, the building energy manager can specify the moments when the optimization is run with consideration of the operational constraints. Accordingly, the main contribution of the paper is to provide and integrate a methodology to minimize the difference between the actual and the desired temperature in each room, as a measure of comfort, respecting constraints that can be easily bounded by building users and manager. The case study considers the real consumption data of an office building which contains 20 lights, 10 ACs, and one dishwasher. Three scenarios have been designed to focus on different functionalities. The outcomes of the paper include proof of the performance of the optimization algorithm and how such a system can effectively minimize electricity consumption by implementing demand response programs and using them in smart grid contexts.
本文提出了一种多周期优化算法，该算法在监控和数据采集系统中实现。该算法控制灯光和空调作为灵活负载，以减少能耗，并控制洗碗机作为可延迟负载，以实现负载转移。在实际建筑运行的几个连续周期中，考虑了几个参数来实现该算法。在所提出的方法中，优化是针对用户舒适度进行的，这在与每个房间内的室内温度相关的目标函数和约束中进行建模，以根据用户的偏好防止过度减少功率。此外，还包括洗碗机的运行周期，并且该算法根据每个周期中的设备重量和可用功率选择最佳的起始点。通过提出的方法，建筑能源经理可以指定在考虑操作约束的情况下运行优化的时刻。因此，本文的主要贡献是提供并整合一种方法，以最小化每个房间实际温度与期望温度之间的差异，作为舒适度的衡量标准，同时尊重建筑用户和经理可以轻松限定的约束条件。案例研究考虑了包含 20 个灯、10 个空调和一个洗碗机的办公大楼的实际消耗数据。设计了三个场景来关注不同的功能。本文的结果包括证明了优化算法的性能，以及如何通过实施需求响应计划并在智能电网环境中使用这些计划来有效降低电力消耗。
